{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Secure Water Treatment (SWaT)\n",
    "\n",
    "- Source and description: https://itrust.sutd.edu.sg/itrust-labs_datasets/dataset_info/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from config import data_raw_folder, data_processed_folder\n",
    "\n",
    "from timeeval import Datasets, DatasetManager\n",
    "from timeeval.datasets import DatasetAnalyzer, DatasetRecord\n",
    "\n",
    "try:\n",
    "    from openpyxl import Workbook\n",
    "except ImportError:\n",
    "    import sys\n",
    "    !conda install --yes --prefix {sys.prefix} openpyxl\n",
    "    from openpyxl import Workbook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for source datasets in /home/projects/akita/data/benchmark-data/data-raw/SWaT and\n",
      "saving processed datasets in /home/projects/akita/data/benchmark-data/data-processed\n"
     ]
    }
   ],
   "source": [
    "source_folder = Path(data_raw_folder) / \"SWaT\"\n",
    "target_folder = Path(data_processed_folder)\n",
    "\n",
    "path_mapping = {\n",
    "    \"SWaT-A1&A2\": (\n",
    "        source_folder / \"SWaT.A1&A2_Dec 2015\" / \"Physical\" / \"SWaT_Dataset_Attack_v0.xlsx\",  # test\n",
    "        source_folder / \"SWaT.A1&A2_Dec 2015\" / \"Physical\" / \"SWaT_Dataset_Normal_v1.xlsx\",  # train\n",
    "    ),\n",
    "    # \"SWaT-A3\": \"\",  # not relevant\n",
    "    # \"SWaT-A4\": \"SWaT.A4&A5_Jul 2019\",  # use A5\n",
    "    \"SWaT-A5\": source_folder / \"SWaT.A4&A5_Jul 2019\" / \"SWaT_dataset_Jul 19 v2.xlsx\",  # only test = unsupervised\n",
    "    \"SWaT-A6\": source_folder / \"SWaT.A6_Dec 2019\" / \"csv\" / \"Dec2019.xlsx\",  # only test = unsupervised\n",
    "    # \"SWaT-A7\": \"\",  # not relevant\n",
    "    # \"SWaT-A8\": \"\",  # note relevant\n",
    "    \n",
    "}\n",
    "\n",
    "# define anomalies\n",
    "anomalies_A5 = {\n",
    "    \"Attack1\": {\n",
    "        \"description\": \"Spoof value of FIT401 from 0.8 to 0.5 to stop de-chlorination by switching off UV401\",\n",
    "        \"targets\": [\"FIT 401\"],\n",
    "        \"begin\": \"2019-07-20 15:08:46+08:00\",\n",
    "        \"end\": \"2019-07-20 15:10:31+08:00\"\n",
    "    },\n",
    "    \"Attack2\": {\n",
    "        \"description\": \" Spoof value of LIT301 from 835 to 1024 to eventually lead to underflow in T301\",\n",
    "        \"targets\": [\"LIT 301\"],\n",
    "        \"begin\": \"2019-07-20 15:15:00+08:00\",\n",
    "        \"end\": \"2019-07-20 15:19:32+08:00\"\n",
    "    },\n",
    "    \"Attack3\": {\n",
    "        \"description\": \"Switch P601 from OFF to ON to increase water in raw water tank\",\n",
    "        \"targets\": [\"P601 Status\"],\n",
    "        \"begin\": \"2019-07-20 15:26:57+08:00\",\n",
    "        \"end\": \"2019-07-20 15:30:48+08:00\"\n",
    "    },\n",
    "    \"Attack4\": {\n",
    "        \"description\": \"Switch from CLOSE to OPEN (MV201) and OFF to ON (P101) to overflow tank T301\",\n",
    "        \"targets\": [\"MV201\", \"P101 Status\"],\n",
    "        \"begin\": \"2019-07-20 15:38:50+08:00\",\n",
    "        \"end\": \"2019-07-20 15:46:20+08:00\"\n",
    "    },\n",
    "    \"Attack5\": {\n",
    "        \"description\": \"Switch MV501 from OPEN to CLOSE to drain water from RO\",\n",
    "        \"targets\": [\"MV 501\"],\n",
    "        \"begin\": \"2019-07-20 15:54:00+08:00\",\n",
    "        \"end\": \"2019-07-20 15:56:00+08:00\"\n",
    "    },\n",
    "    \"Attack6\": {\n",
    "        \"description\": \"Switch P301 from ON to OFF to halt stage 3 (UF process)\",\n",
    "        \"targets\": [\"P301 Status\"],\n",
    "        \"begin\": \"2019-07-20 16:02:56+08:00\",\n",
    "        \"end\": \"2019-07-20 16:16:18+08:00\"\n",
    "    },\n",
    "}\n",
    "\n",
    "for a in anomalies_A5:\n",
    "    for dt_key in [\"begin\", \"end\"]:\n",
    "        dt = anomalies_A5[a][dt_key]\n",
    "        # convert times to UTZ-datetimes\n",
    "        dt = pd.to_datetime(dt).astimezone(tz=\"UTC\")\n",
    "        \n",
    "        # fix anomaly labels (timestamps)\n",
    "        dt = dt - pd.Timedelta(2, unit=\"m\") + pd.Timedelta(14, unit=\"s\")\n",
    "        anomalies_A5[a][dt_key] = dt\n",
    "\n",
    "    if a == \"Attack1\":\n",
    "        anomalies_A5[a][\"begin\"] -= pd.Timedelta(2, unit=\"s\")\n",
    "        anomalies_A5[a][\"end\"] -= pd.Timedelta(1, unit=\"s\")\n",
    "    elif a == \"Attack2\":\n",
    "        anomalies_A5[a][\"begin\"] += pd.Timedelta(5, unit=\"s\")\n",
    "    elif a == \"Attack4\":\n",
    "        anomalies_A5[a][\"end\"] += pd.Timedelta(30, unit=\"s\")\n",
    "    elif a == \"Attack5\":\n",
    "        anomalies_A5[a][\"begin\"] += pd.Timedelta(3, unit=\"s\")\n",
    "        anomalies_A5[a][\"end\"] += pd.Timedelta(40, unit=\"s\")\n",
    "    elif a == \"Attack6\":\n",
    "        anomalies_A5[a][\"begin\"] -= pd.Timedelta(5, unit=\"s\")\n",
    "        anomalies_A5[a][\"end\"] += pd.Timedelta(35, unit=\"s\")\n",
    "\n",
    "anomalies_A6 = {\n",
    "    \"C2Com&Attack5\": {\n",
    "        \"description\": \"Infiltrate SCADA WS with second payload from C2, and disrupt sensors cycle 1\",\n",
    "        \"begin\": \"12:30\",\n",
    "        \"end\": \"12:33\"\n",
    "    },\n",
    "    \"Attack6\": {\n",
    "        \"description\": \"Disrupt sensors cycle 2\",\n",
    "        \"begin\": \"12:43\",\n",
    "        \"end\": \"12:46\"\n",
    "    },\n",
    "    \"Attack7\": {\n",
    "        \"description\": \"Disrupt sensors cycle 3\",\n",
    "        \"begin\": \"12:56\",\n",
    "        \"end\": \"12:59\"\n",
    "    },\n",
    "    \"Attack8\": {\n",
    "        \"description\": \"Disrupt sensors cycle 4\",\n",
    "        \"begin\": \"13:09\",\n",
    "        \"end\": \"13:12\"\n",
    "    },\n",
    "    \"Attack9\": {\n",
    "        \"description\": \"Disrupt sensors cycle 5\",\n",
    "        \"begin\": \"13:22\",\n",
    "        \"end\": \"13:25\"\n",
    "    }\n",
    "}\n",
    "\n",
    "date = \"2019-12-06\"\n",
    "tz = \"Asia/Hong_Kong\"\n",
    "for a in anomalies_A6:\n",
    "    for dt_key in [\"begin\", \"end\"]:\n",
    "        # convert times to datetimes\n",
    "        dt = anomalies_A6[a][dt_key]\n",
    "        dt = pd.to_datetime(f\"{date} {dt}:00\").tz_localize(tz=tz)\n",
    "        anomalies_A6[a][dt_key] = dt\n",
    "\n",
    "print(f\"Looking for source datasets in {Path(source_folder).absolute()} and\\nsaving processed datasets in {Path(target_folder).absolute()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created directories /home/projects/akita/data/benchmark-data/data-processed/multivariate/SWaT\n"
     ]
    }
   ],
   "source": [
    "# shared by all datasets\n",
    "dataset_collection_name = \"SWaT\"\n",
    "dataset_type = \"real\"\n",
    "input_type = \"multivariate\"\n",
    "datetime_index = True\n",
    "split_at = None\n",
    "\n",
    "# create target subfolder\n",
    "dataset_subfolder = Path(input_type) / dataset_collection_name\n",
    "target_subfolder = target_folder / dataset_subfolder\n",
    "target_subfolder.mkdir(parents=True, exist_ok=True)\n",
    "print(f\"Created directories {target_subfolder}\")\n",
    "\n",
    "dm = DatasetManager(target_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_anomalies(df: pd.DataFrame, anomalies: dict) -> pd.DataFrame:\n",
    "    df = df.set_index(\"timestamp\")\n",
    "    df[\"is_anomaly\"] = 0\n",
    "    for a in anomalies:\n",
    "        begin = anomalies[a][\"begin\"]\n",
    "        end = anomalies[a][\"end\"]\n",
    "        df.loc[begin:end, \"is_anomaly\"] = 1\n",
    "    df = df.reset_index()\n",
    "    return df\n",
    "\n",
    "def prepare_dataset_A1_A2(dataset_path: Path) -> pd.DataFrame:\n",
    "    df = pd.read_excel(\n",
    "        dataset_path,\n",
    "        sheet_name=0,\n",
    "        skiprows=1,\n",
    "        header=0,\n",
    "        index_col=None,\n",
    "        parse_dates=[0]\n",
    "    )\n",
    "    df = df.rename(columns={\" Timestamp\": \"timestamp\"})\n",
    "    df[\"is_anomaly\"] = 0\n",
    "    df.loc[df[\"Normal/Attack\"] == \"Attack\", \"is_anomaly\"] = 1\n",
    "    df = df.drop(columns=[\"Normal/Attack\"])\n",
    "    return df\n",
    "\n",
    "def prepare_dataset_A5(dataset_path: Path) -> pd.DataFrame:\n",
    "    df = pd.read_excel(\n",
    "        dataset_path,\n",
    "        sheet_name=0,\n",
    "        skiprows=1,\n",
    "        header=[0, 1],\n",
    "        index_col=None,\n",
    "        parse_dates=[0]\n",
    "    )\n",
    "    # align header\n",
    "    df.columns = list(df.columns.get_level_values(1)[0:1]) + list(df.columns.get_level_values(0)[1:])\n",
    "\n",
    "    # round timestamps to have equi-distant steps (= seconds)\n",
    "    df[\"timestamp\"] = df[\"timestamp\"].map(lambda dt: dt.floor(freq=\"S\"))\n",
    "\n",
    "    # make actuator status numeric\n",
    "    df = df.replace(\"Inactive\", 0)\n",
    "    df = df.replace(\"Active\", 1)\n",
    "\n",
    "    # add anomaly labels\n",
    "    df = add_anomalies(df, anomalies_A5)\n",
    "    return df\n",
    "\n",
    "def prepare_dataset_A6(dataset_path: Path) -> pd.DataFrame:\n",
    "    df = pd.read_excel(\n",
    "        dataset_path,\n",
    "        sheet_name=0,\n",
    "        skiprows=9,\n",
    "        header=0,\n",
    "        index_col=None,\n",
    "        parse_dates=[0]\n",
    "    )\n",
    "\n",
    "    # localize and convert timestamp\n",
    "    s = df[\"t_stamp\"]\n",
    "    s = s.map(lambda dt: dt.tz_localize(tz=\"Asia/Hong_Kong\"))\n",
    "    df.insert(0, \"timestamp\", s)\n",
    "    df.drop(columns=[\"t_stamp\"], inplace=True)\n",
    "\n",
    "    # make actuator status numeric\n",
    "    df = df.replace(\"Inactive\", 0)\n",
    "    df = df.replace(\"Active\", 1)\n",
    "\n",
    "    # add anomaly labels\n",
    "    df = add_anomalies(df, anomalies_A6)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A1 and A2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Processing dataset SWaT-A1&A2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sebastian.schmidl/.conda/envs/timeeval/lib/python3.8/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
      "  warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  written test dataset\n",
      "  analyzed test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sebastian.schmidl/.conda/envs/timeeval/lib/python3.8/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
      "  warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  written training dataset\n",
      "  analyzed training dataset\n",
      "... processed dataset SWaT-A1&A2\n"
     ]
    }
   ],
   "source": [
    "dataset_name = \"SWaT-A1&A2\"\n",
    "train_type = \"semi-supervised\"\n",
    "train_is_normal = True\n",
    "\n",
    "print(f\"-> Processing dataset {dataset_name}\")\n",
    "\n",
    "test_filename = f\"{dataset_name}.test.csv\"\n",
    "train_filename = f\"{dataset_name}.train.csv\"\n",
    "test_path = dataset_subfolder / test_filename\n",
    "train_path = dataset_subfolder / train_filename\n",
    "target_test_filepath = target_subfolder / test_filename\n",
    "target_train_filepath = target_subfolder / train_filename\n",
    "target_meta_filepath = target_test_filepath.parent / f\"{dataset_name}.{Datasets.METADATA_FILENAME_SUFFIX}\"\n",
    "\n",
    "# prepare test dataset\n",
    "source_paths = path_mapping[dataset_name]\n",
    "df_test = prepare_dataset_A1_A2(source_paths[0])\n",
    "df_test.to_csv(target_test_filepath, index=False)\n",
    "print(\"  written test dataset\")\n",
    "\n",
    "da = DatasetAnalyzer((dataset_collection_name, dataset_name), is_train=False, df=df_test, ignore_stationarity=True)\n",
    "da.save_to_json(target_meta_filepath, overwrite=True)\n",
    "meta = da.metadata\n",
    "del da\n",
    "del df_test\n",
    "print(\"  analyzed test dataset\")\n",
    "\n",
    "# prepare train dataset\n",
    "df_train = prepare_dataset_A1_A2(source_paths[1])\n",
    "df_train.to_csv(target_train_filepath, index=False)\n",
    "print(f\"  written training dataset\")\n",
    "\n",
    "DatasetAnalyzer((dataset_collection_name, dataset_name), is_train=True, df=df_train, ignore_stationarity=True)\\\n",
    "    .save_to_json(target_meta_filepath, overwrite=False)\n",
    "del df_train\n",
    "print(f\"  analyzed training dataset\")\n",
    "\n",
    "dm.add_dataset(DatasetRecord(\n",
    "      collection_name=dataset_collection_name,\n",
    "      dataset_name=dataset_name,\n",
    "      train_path=train_path,\n",
    "      test_path=test_path,\n",
    "      dataset_type=dataset_type,\n",
    "      datetime_index=datetime_index,\n",
    "      split_at=split_at,\n",
    "      train_type=train_type,\n",
    "      train_is_normal=train_is_normal,\n",
    "      input_type=input_type,\n",
    "      length=meta.length,\n",
    "      dimensions=meta.dimensions,\n",
    "      contamination=meta.contamination,\n",
    "      num_anomalies=meta.num_anomalies,\n",
    "      min_anomaly_length=meta.anomaly_length.min,\n",
    "      median_anomaly_length=meta.anomaly_length.median,\n",
    "      max_anomaly_length=meta.anomaly_length.max,\n",
    "      mean=meta.mean,\n",
    "      stddev=meta.stddev,\n",
    "      trend=meta.trend,\n",
    "      stationarity=meta.get_stationarity_name(),\n",
    "      period_size=np.nan\n",
    "))\n",
    "print(f\"... processed dataset {dataset_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Processing dataset SWaT-A5\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P102 Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for LS 201 encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for LS 202 encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for LSL 203 encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for LSLL 203 encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P2_STATE encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P201 Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P202 Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P204 Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P206 Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P207 Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P208 Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P302 Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for AIT 401 encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for LS 401 encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P4_STATE encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P402 Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P403 Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P404 Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for MV 502 encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for MV 503 encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for MV 504 encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P5_STATE encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P501 Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P502 Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for LSH 602 encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for LSH 603 encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for LSL 601 encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for LSL 602 encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for LSL 603 encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P6 STATE encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P602 Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A5') (test)] KPSS trend stationarity test for P603 Status encountered an error: cannot convert float NaN to integer\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed test dataset\n",
      "... processed dataset SWaT-A5\n"
     ]
    }
   ],
   "source": [
    "dataset_name = \"SWaT-A5\"\n",
    "train_type = \"unsupervised\"\n",
    "train_is_normal = False\n",
    "\n",
    "print(f\"-> Processing dataset {dataset_name}\")\n",
    "\n",
    "test_filename = f\"{dataset_name}.test.csv\"\n",
    "test_path = dataset_subfolder / test_filename\n",
    "target_test_filepath = target_subfolder / test_filename\n",
    "target_meta_filepath = target_test_filepath.parent / f\"{dataset_name}.{Datasets.METADATA_FILENAME_SUFFIX}\"\n",
    "\n",
    "# prepare test dataset\n",
    "source_path = path_mapping[dataset_name]\n",
    "df_test = prepare_dataset_A5(source_path)\n",
    "df_test.to_csv(target_test_filepath, index=False)\n",
    "print(\"  written test dataset\")\n",
    "\n",
    "da = DatasetAnalyzer((dataset_collection_name, dataset_name), is_train=False, df=df_test)\n",
    "da.save_to_json(target_meta_filepath, overwrite=True)\n",
    "meta = da.metadata\n",
    "del da\n",
    "del df_test\n",
    "print(\"  analyzed test dataset\")\n",
    "\n",
    "dm.add_dataset(DatasetRecord(\n",
    "      collection_name=dataset_collection_name,\n",
    "      dataset_name=dataset_name,\n",
    "      train_path=None,\n",
    "      test_path=test_path,\n",
    "      dataset_type=dataset_type,\n",
    "      datetime_index=datetime_index,\n",
    "      split_at=split_at,\n",
    "      train_type=train_type,\n",
    "      train_is_normal=train_is_normal,\n",
    "      input_type=input_type,\n",
    "      length=meta.length,\n",
    "      dimensions=meta.dimensions,\n",
    "      contamination=meta.contamination,\n",
    "      num_anomalies=meta.num_anomalies,\n",
    "      min_anomaly_length=meta.anomaly_length.min,\n",
    "      median_anomaly_length=meta.anomaly_length.median,\n",
    "      max_anomaly_length=meta.anomaly_length.max,\n",
    "      mean=meta.mean,\n",
    "      stddev=meta.stddev,\n",
    "      trend=meta.trend,\n",
    "      stationarity=meta.get_stationarity_name(),\n",
    "      period_size=np.nan\n",
    "))\n",
    "print(f\"... processed dataset {dataset_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Processing dataset SWaT-A6\n",
      "  written test dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('SWaT', 'SWaT-A6') (test)] ADF stationarity test for P101.Status encountered an error: exog contains inf or nans\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P101.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P102.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P2_STATE encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P202.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P204.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P205.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P206.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P207.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P208.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for LS201.Alarm encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for LS202.Alarm encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for LSL203.Alarm encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for LSLL203.Alarm encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P302.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for PSH301.Alarm encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for DPSH301.Alarm encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P4_STATE encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for AIT401.Pv encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P401.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P402.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P403.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P404.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for UV401.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for LS401.Alarm encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P5_STATE encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for FIT504.Pv encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P501.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P502.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for MV501.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for MV502.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for MV503.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for MV504.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for PSH501.Alarm encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for PSL501.Alarm encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P6_STATE encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for P603.Status encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for LSL601.Alarm encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for LSH602.Alarm encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for LSL602.Alarm encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for LSH603.Alarm encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] KPSS trend stationarity test for LSL603.Alarm encountered an error: cannot convert float NaN to integer\n",
      "[('SWaT', 'SWaT-A6') (test)] trend analysis for P101.Status encountered an error: Input contains NaN, infinity or a value too large for dtype('float64').\n",
      "[('SWaT', 'SWaT-A6') (test)] trend analysis for P101.Status encountered an error: Input contains NaN, infinity or a value too large for dtype('float64').\n",
      "[('SWaT', 'SWaT-A6') (test)] trend analysis for P101.Status encountered an error: Input contains NaN, infinity or a value too large for dtype('float64').\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed test dataset\n",
      "... processed dataset SWaT-A6\n"
     ]
    }
   ],
   "source": [
    "dataset_name = \"SWaT-A6\"\n",
    "train_type = \"unsupervised\"\n",
    "train_is_normal = False\n",
    "\n",
    "print(f\"-> Processing dataset {dataset_name}\")\n",
    "\n",
    "test_filename = f\"{dataset_name}.test.csv\"\n",
    "test_path = dataset_subfolder / test_filename\n",
    "target_test_filepath = target_subfolder / test_filename\n",
    "target_meta_filepath = target_test_filepath.parent / f\"{dataset_name}.{Datasets.METADATA_FILENAME_SUFFIX}\"\n",
    "\n",
    "# prepare test dataset\n",
    "source_path = path_mapping[dataset_name]\n",
    "df_test = prepare_dataset_A6(source_path)\n",
    "df_test.to_csv(target_test_filepath, index=False)\n",
    "print(\"  written test dataset\")\n",
    "\n",
    "da = DatasetAnalyzer((dataset_collection_name, dataset_name), is_train=False, df=df_test)\n",
    "da.save_to_json(target_meta_filepath, overwrite=True)\n",
    "meta = da.metadata\n",
    "del da\n",
    "del df_test\n",
    "print(\"  analyzed test dataset\")\n",
    "\n",
    "dm.add_dataset(DatasetRecord(\n",
    "      collection_name=dataset_collection_name,\n",
    "      dataset_name=dataset_name,\n",
    "      train_path=None,\n",
    "      test_path=test_path,\n",
    "      dataset_type=dataset_type,\n",
    "      datetime_index=datetime_index,\n",
    "      split_at=split_at,\n",
    "      train_type=train_type,\n",
    "      train_is_normal=train_is_normal,\n",
    "      input_type=input_type,\n",
    "      length=meta.length,\n",
    "      dimensions=meta.dimensions,\n",
    "      contamination=meta.contamination,\n",
    "      num_anomalies=meta.num_anomalies,\n",
    "      min_anomaly_length=meta.anomaly_length.min,\n",
    "      median_anomaly_length=meta.anomaly_length.median,\n",
    "      max_anomaly_length=meta.anomaly_length.max,\n",
    "      mean=meta.mean,\n",
    "      stddev=meta.stddev,\n",
    "      trend=meta.trend,\n",
    "      stationarity=meta.get_stationarity_name(),\n",
    "      period_size=np.nan\n",
    "))\n",
    "print(f\"... processed dataset {dataset_name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dm.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>train_path</th>\n",
       "      <th>test_path</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>datetime_index</th>\n",
       "      <th>split_at</th>\n",
       "      <th>train_type</th>\n",
       "      <th>train_is_normal</th>\n",
       "      <th>input_type</th>\n",
       "      <th>length</th>\n",
       "      <th>dimensions</th>\n",
       "      <th>contamination</th>\n",
       "      <th>num_anomalies</th>\n",
       "      <th>min_anomaly_length</th>\n",
       "      <th>median_anomaly_length</th>\n",
       "      <th>max_anomaly_length</th>\n",
       "      <th>mean</th>\n",
       "      <th>stddev</th>\n",
       "      <th>trend</th>\n",
       "      <th>stationarity</th>\n",
       "      <th>period_size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection_name</th>\n",
       "      <th>dataset_name</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">SWaT</th>\n",
       "      <th>SWaT-A1&amp;A2</th>\n",
       "      <td>multivariate/SWaT/SWaT-A1&amp;A2.train.csv</td>\n",
       "      <td>multivariate/SWaT/SWaT-A1&amp;A2.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>semi-supervised</td>\n",
       "      <td>True</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>449919</td>\n",
       "      <td>51</td>\n",
       "      <td>0.121320</td>\n",
       "      <td>35</td>\n",
       "      <td>101</td>\n",
       "      <td>444</td>\n",
       "      <td>35900</td>\n",
       "      <td>80.458572</td>\n",
       "      <td>12.783598</td>\n",
       "      <td>kubic trend</td>\n",
       "      <td>not_stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWaT-A5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/SWaT/SWaT-A5.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>14996</td>\n",
       "      <td>77</td>\n",
       "      <td>0.139304</td>\n",
       "      <td>6</td>\n",
       "      <td>107</td>\n",
       "      <td>250</td>\n",
       "      <td>843</td>\n",
       "      <td>64.099889</td>\n",
       "      <td>5.353307</td>\n",
       "      <td>kubic trend</td>\n",
       "      <td>not_stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWaT-A6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/SWaT/SWaT-A6.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>13201</td>\n",
       "      <td>81</td>\n",
       "      <td>0.068555</td>\n",
       "      <td>5</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>48.592063</td>\n",
       "      <td>4.026858</td>\n",
       "      <td>kubic trend</td>\n",
       "      <td>not_stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          train_path  \\\n",
       "collection_name dataset_name                                           \n",
       "SWaT            SWaT-A1&A2    multivariate/SWaT/SWaT-A1&A2.train.csv   \n",
       "                SWaT-A5                                          NaN   \n",
       "                SWaT-A6                                          NaN   \n",
       "\n",
       "                                                          test_path  \\\n",
       "collection_name dataset_name                                          \n",
       "SWaT            SWaT-A1&A2    multivariate/SWaT/SWaT-A1&A2.test.csv   \n",
       "                SWaT-A5          multivariate/SWaT/SWaT-A5.test.csv   \n",
       "                SWaT-A6          multivariate/SWaT/SWaT-A6.test.csv   \n",
       "\n",
       "                             dataset_type  datetime_index  split_at  \\\n",
       "collection_name dataset_name                                          \n",
       "SWaT            SWaT-A1&A2           real            True       NaN   \n",
       "                SWaT-A5              real            True       NaN   \n",
       "                SWaT-A6              real            True       NaN   \n",
       "\n",
       "                                   train_type  train_is_normal    input_type  \\\n",
       "collection_name dataset_name                                                   \n",
       "SWaT            SWaT-A1&A2    semi-supervised             True  multivariate   \n",
       "                SWaT-A5          unsupervised            False  multivariate   \n",
       "                SWaT-A6          unsupervised            False  multivariate   \n",
       "\n",
       "                              length  dimensions  contamination  \\\n",
       "collection_name dataset_name                                      \n",
       "SWaT            SWaT-A1&A2    449919          51       0.121320   \n",
       "                SWaT-A5        14996          77       0.139304   \n",
       "                SWaT-A6        13201          81       0.068555   \n",
       "\n",
       "                              num_anomalies  min_anomaly_length  \\\n",
       "collection_name dataset_name                                      \n",
       "SWaT            SWaT-A1&A2               35                 101   \n",
       "                SWaT-A5                   6                 107   \n",
       "                SWaT-A6                   5                 181   \n",
       "\n",
       "                              median_anomaly_length  max_anomaly_length  \\\n",
       "collection_name dataset_name                                              \n",
       "SWaT            SWaT-A1&A2                      444               35900   \n",
       "                SWaT-A5                         250                 843   \n",
       "                SWaT-A6                         181                 181   \n",
       "\n",
       "                                   mean     stddev        trend  \\\n",
       "collection_name dataset_name                                      \n",
       "SWaT            SWaT-A1&A2    80.458572  12.783598  kubic trend   \n",
       "                SWaT-A5       64.099889   5.353307  kubic trend   \n",
       "                SWaT-A6       48.592063   4.026858  kubic trend   \n",
       "\n",
       "                                stationarity  period_size  \n",
       "collection_name dataset_name                               \n",
       "SWaT            SWaT-A1&A2    not_stationary          NaN  \n",
       "                SWaT-A5       not_stationary          NaN  \n",
       "                SWaT-A6       not_stationary          NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.refresh()\n",
    "dm.df().loc[(slice(dataset_collection_name,dataset_collection_name), slice(None))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Exploration\n",
    "\n",
    "### A1 and A2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sebastian.schmidl/.conda/envs/timeeval/lib/python3.8/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
      "  warn(msg)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>FIT101</th>\n",
       "      <th>LIT101</th>\n",
       "      <th>MV101</th>\n",
       "      <th>P101</th>\n",
       "      <th>P102</th>\n",
       "      <th>AIT201</th>\n",
       "      <th>AIT202</th>\n",
       "      <th>AIT203</th>\n",
       "      <th>FIT201</th>\n",
       "      <th>...</th>\n",
       "      <th>P501</th>\n",
       "      <th>P502</th>\n",
       "      <th>PIT501</th>\n",
       "      <th>PIT502</th>\n",
       "      <th>PIT503</th>\n",
       "      <th>FIT601</th>\n",
       "      <th>P601</th>\n",
       "      <th>P602</th>\n",
       "      <th>P603</th>\n",
       "      <th>is_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-28 10:00:00</td>\n",
       "      <td>2.427057</td>\n",
       "      <td>522.8467</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>262.0161</td>\n",
       "      <td>8.396437</td>\n",
       "      <td>328.6337</td>\n",
       "      <td>2.445391</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>250.8652</td>\n",
       "      <td>1.649953</td>\n",
       "      <td>189.5988</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-28 10:00:01</td>\n",
       "      <td>2.446274</td>\n",
       "      <td>522.8860</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>262.0161</td>\n",
       "      <td>8.396437</td>\n",
       "      <td>328.6337</td>\n",
       "      <td>2.445391</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>250.8652</td>\n",
       "      <td>1.649953</td>\n",
       "      <td>189.6789</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-28 10:00:02</td>\n",
       "      <td>2.489191</td>\n",
       "      <td>522.8467</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>262.0161</td>\n",
       "      <td>8.394514</td>\n",
       "      <td>328.6337</td>\n",
       "      <td>2.442316</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>250.8812</td>\n",
       "      <td>1.649953</td>\n",
       "      <td>189.6789</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-28 10:00:03</td>\n",
       "      <td>2.534350</td>\n",
       "      <td>522.9645</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>262.0161</td>\n",
       "      <td>8.394514</td>\n",
       "      <td>328.6337</td>\n",
       "      <td>2.442316</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>250.8812</td>\n",
       "      <td>1.649953</td>\n",
       "      <td>189.6148</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-28 10:00:04</td>\n",
       "      <td>2.569260</td>\n",
       "      <td>523.4748</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>262.0161</td>\n",
       "      <td>8.394514</td>\n",
       "      <td>328.6337</td>\n",
       "      <td>2.443085</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>250.8812</td>\n",
       "      <td>1.649953</td>\n",
       "      <td>189.5027</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449914</th>\n",
       "      <td>2016-02-01 14:59:55</td>\n",
       "      <td>2.559972</td>\n",
       "      <td>519.5495</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>168.0979</td>\n",
       "      <td>8.638683</td>\n",
       "      <td>301.9226</td>\n",
       "      <td>2.459488</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>251.1535</td>\n",
       "      <td>0.865024</td>\n",
       "      <td>189.0220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449915</th>\n",
       "      <td>2016-02-01 14:59:56</td>\n",
       "      <td>2.549082</td>\n",
       "      <td>520.4131</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>168.0979</td>\n",
       "      <td>8.638683</td>\n",
       "      <td>301.9226</td>\n",
       "      <td>2.459488</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>251.0734</td>\n",
       "      <td>0.865024</td>\n",
       "      <td>188.9259</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449916</th>\n",
       "      <td>2016-02-01 14:59:57</td>\n",
       "      <td>2.531467</td>\n",
       "      <td>520.6878</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>168.0979</td>\n",
       "      <td>8.638683</td>\n",
       "      <td>301.9226</td>\n",
       "      <td>2.460129</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>251.0734</td>\n",
       "      <td>0.865024</td>\n",
       "      <td>188.9259</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449917</th>\n",
       "      <td>2016-02-01 14:59:58</td>\n",
       "      <td>2.521218</td>\n",
       "      <td>520.7271</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>168.0979</td>\n",
       "      <td>8.638683</td>\n",
       "      <td>301.9226</td>\n",
       "      <td>2.460129</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>251.0734</td>\n",
       "      <td>0.865024</td>\n",
       "      <td>188.9259</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449918</th>\n",
       "      <td>2016-02-01 14:59:59</td>\n",
       "      <td>2.501681</td>\n",
       "      <td>521.1196</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>168.0979</td>\n",
       "      <td>8.638683</td>\n",
       "      <td>301.9226</td>\n",
       "      <td>2.458206</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>251.0734</td>\n",
       "      <td>0.865024</td>\n",
       "      <td>188.9259</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>449919 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Timestamp    FIT101    LIT101   MV101  P101  P102    AIT201  \\\n",
       "0      2015-12-28 10:00:00  2.427057  522.8467       2     2     1  262.0161   \n",
       "1      2015-12-28 10:00:01  2.446274  522.8860       2     2     1  262.0161   \n",
       "2      2015-12-28 10:00:02  2.489191  522.8467       2     2     1  262.0161   \n",
       "3      2015-12-28 10:00:03  2.534350  522.9645       2     2     1  262.0161   \n",
       "4      2015-12-28 10:00:04  2.569260  523.4748       2     2     1  262.0161   \n",
       "...                    ...       ...       ...     ...   ...   ...       ...   \n",
       "449914 2016-02-01 14:59:55  2.559972  519.5495       2     2     1  168.0979   \n",
       "449915 2016-02-01 14:59:56  2.549082  520.4131       2     2     1  168.0979   \n",
       "449916 2016-02-01 14:59:57  2.531467  520.6878       2     2     1  168.0979   \n",
       "449917 2016-02-01 14:59:58  2.521218  520.7271       2     2     1  168.0979   \n",
       "449918 2016-02-01 14:59:59  2.501681  521.1196       2     2     1  168.0979   \n",
       "\n",
       "          AIT202    AIT203    FIT201  ...  P501  P502    PIT501    PIT502  \\\n",
       "0       8.396437  328.6337  2.445391  ...     2     1  250.8652  1.649953   \n",
       "1       8.396437  328.6337  2.445391  ...     2     1  250.8652  1.649953   \n",
       "2       8.394514  328.6337  2.442316  ...     2     1  250.8812  1.649953   \n",
       "3       8.394514  328.6337  2.442316  ...     2     1  250.8812  1.649953   \n",
       "4       8.394514  328.6337  2.443085  ...     2     1  250.8812  1.649953   \n",
       "...          ...       ...       ...  ...   ...   ...       ...       ...   \n",
       "449914  8.638683  301.9226  2.459488  ...     2     1  251.1535  0.865024   \n",
       "449915  8.638683  301.9226  2.459488  ...     2     1  251.0734  0.865024   \n",
       "449916  8.638683  301.9226  2.460129  ...     2     1  251.0734  0.865024   \n",
       "449917  8.638683  301.9226  2.460129  ...     2     1  251.0734  0.865024   \n",
       "449918  8.638683  301.9226  2.458206  ...     2     1  251.0734  0.865024   \n",
       "\n",
       "          PIT503    FIT601  P601  P602  P603  is_anomaly  \n",
       "0       189.5988  0.000128     1     1     1           0  \n",
       "1       189.6789  0.000128     1     1     1           0  \n",
       "2       189.6789  0.000128     1     1     1           0  \n",
       "3       189.6148  0.000128     1     1     1           0  \n",
       "4       189.5027  0.000128     1     1     1           0  \n",
       "...          ...       ...   ...   ...   ...         ...  \n",
       "449914  189.0220  0.000000     1     1     1           0  \n",
       "449915  188.9259  0.000000     1     1     1           0  \n",
       "449916  188.9259  0.000000     1     1     1           0  \n",
       "449917  188.9259  0.000000     1     1     1           0  \n",
       "449918  188.9259  0.000000     1     1     1           0  \n",
       "\n",
       "[449919 rows x 53 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\n",
    "    source_folder / \"SWaT.A1&A2_Dec 2015\" / \"Physical\" / \"SWaT_Dataset_Attack_v0.xlsx\",\n",
    "    sheet_name=0,\n",
    "    skiprows=1,\n",
    "    header=0,\n",
    "    index_col=None,\n",
    "    parse_dates=[0]\n",
    ")\n",
    "df[\"is_anomaly\"] = 0\n",
    "df.loc[df[\"Normal/Attack\"] == \"Attack\", \"is_anomaly\"] = 1\n",
    "df = df.drop(columns=[\"Normal/Attack\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tmp = df[[\"FIT101\", \"AIT203\", \"AIT203\", \"PIT503\", \"is_anomaly\"]]\n",
    "df_tmp.iloc[:, :-1].plot()\n",
    "s = df_tmp[\"is_anomaly\"].diff()\n",
    "for begin, end in zip(s[s == -1].index, s[s == 1].index):\n",
    "    plt.gca().add_patch(matplotlib.patches.Rectangle((begin, 0), end - begin, df_tmp.max().max(), color=\"red\", alpha=0.25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sebastian.schmidl/.conda/envs/timeeval/lib/python3.8/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
      "  warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>FIT101</th>\n",
       "      <th>LIT101</th>\n",
       "      <th>MV101</th>\n",
       "      <th>P101</th>\n",
       "      <th>P102</th>\n",
       "      <th>AIT201</th>\n",
       "      <th>AIT202</th>\n",
       "      <th>AIT203</th>\n",
       "      <th>FIT201</th>\n",
       "      <th>...</th>\n",
       "      <th>P501</th>\n",
       "      <th>P502</th>\n",
       "      <th>PIT501</th>\n",
       "      <th>PIT502</th>\n",
       "      <th>PIT503</th>\n",
       "      <th>FIT601</th>\n",
       "      <th>P601</th>\n",
       "      <th>P602</th>\n",
       "      <th>P603</th>\n",
       "      <th>is_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-22 16:30:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>124.3135</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>251.9226</td>\n",
       "      <td>8.313446</td>\n",
       "      <td>312.7916</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.100231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.3485</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-22 16:30:01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>124.3920</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>251.9226</td>\n",
       "      <td>8.313446</td>\n",
       "      <td>312.7916</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.100231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.3485</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-22 16:30:02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>124.4705</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>251.9226</td>\n",
       "      <td>8.313446</td>\n",
       "      <td>312.7916</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.100231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.3485</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-22 16:30:03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>124.6668</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>251.9226</td>\n",
       "      <td>8.313446</td>\n",
       "      <td>312.7916</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.100231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.3485</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-22 16:30:04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>124.5098</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>251.9226</td>\n",
       "      <td>8.313446</td>\n",
       "      <td>312.7916</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.100231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.3485</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494995</th>\n",
       "      <td>2015-12-28 09:59:55</td>\n",
       "      <td>2.460366</td>\n",
       "      <td>523.0430</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>262.0161</td>\n",
       "      <td>8.396437</td>\n",
       "      <td>328.5055</td>\n",
       "      <td>2.442316</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>250.817100</td>\n",
       "      <td>1.778105</td>\n",
       "      <td>189.8552</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494996</th>\n",
       "      <td>2015-12-28 09:59:56</td>\n",
       "      <td>2.448836</td>\n",
       "      <td>522.9645</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>262.0161</td>\n",
       "      <td>8.396437</td>\n",
       "      <td>328.5055</td>\n",
       "      <td>2.442316</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>250.817100</td>\n",
       "      <td>1.778105</td>\n",
       "      <td>189.5027</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494997</th>\n",
       "      <td>2015-12-28 09:59:57</td>\n",
       "      <td>2.434744</td>\n",
       "      <td>522.8860</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>262.0161</td>\n",
       "      <td>8.396437</td>\n",
       "      <td>328.6337</td>\n",
       "      <td>2.444879</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>250.817100</td>\n",
       "      <td>1.778105</td>\n",
       "      <td>189.5027</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494998</th>\n",
       "      <td>2015-12-28 09:59:58</td>\n",
       "      <td>2.428338</td>\n",
       "      <td>522.9252</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>262.0161</td>\n",
       "      <td>8.396437</td>\n",
       "      <td>328.6337</td>\n",
       "      <td>2.445391</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>250.817100</td>\n",
       "      <td>1.649953</td>\n",
       "      <td>189.5027</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494999</th>\n",
       "      <td>2015-12-28 09:59:59</td>\n",
       "      <td>2.427057</td>\n",
       "      <td>522.8467</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>262.0161</td>\n",
       "      <td>8.396437</td>\n",
       "      <td>328.6337</td>\n",
       "      <td>2.445391</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>250.865200</td>\n",
       "      <td>1.649953</td>\n",
       "      <td>189.5988</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>495000 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Timestamp    FIT101    LIT101  MV101  P101  P102    AIT201  \\\n",
       "0      2015-12-22 16:30:00  0.000000  124.3135      1     1     1  251.9226   \n",
       "1      2015-12-22 16:30:01  0.000000  124.3920      1     1     1  251.9226   \n",
       "2      2015-12-22 16:30:02  0.000000  124.4705      1     1     1  251.9226   \n",
       "3      2015-12-22 16:30:03  0.000000  124.6668      1     1     1  251.9226   \n",
       "4      2015-12-22 16:30:04  0.000000  124.5098      1     1     1  251.9226   \n",
       "...                    ...       ...       ...    ...   ...   ...       ...   \n",
       "494995 2015-12-28 09:59:55  2.460366  523.0430      2     2     1  262.0161   \n",
       "494996 2015-12-28 09:59:56  2.448836  522.9645      2     2     1  262.0161   \n",
       "494997 2015-12-28 09:59:57  2.434744  522.8860      2     2     1  262.0161   \n",
       "494998 2015-12-28 09:59:58  2.428338  522.9252      2     2     1  262.0161   \n",
       "494999 2015-12-28 09:59:59  2.427057  522.8467      2     2     1  262.0161   \n",
       "\n",
       "          AIT202    AIT203    FIT201  ...  P501  P502      PIT501    PIT502  \\\n",
       "0       8.313446  312.7916  0.000000  ...     1     1    9.100231  0.000000   \n",
       "1       8.313446  312.7916  0.000000  ...     1     1    9.100231  0.000000   \n",
       "2       8.313446  312.7916  0.000000  ...     1     1    9.100231  0.000000   \n",
       "3       8.313446  312.7916  0.000000  ...     1     1    9.100231  0.000000   \n",
       "4       8.313446  312.7916  0.000000  ...     1     1    9.100231  0.000000   \n",
       "...          ...       ...       ...  ...   ...   ...         ...       ...   \n",
       "494995  8.396437  328.5055  2.442316  ...     2     1  250.817100  1.778105   \n",
       "494996  8.396437  328.5055  2.442316  ...     2     1  250.817100  1.778105   \n",
       "494997  8.396437  328.6337  2.444879  ...     2     1  250.817100  1.778105   \n",
       "494998  8.396437  328.6337  2.445391  ...     2     1  250.817100  1.649953   \n",
       "494999  8.396437  328.6337  2.445391  ...     2     1  250.865200  1.649953   \n",
       "\n",
       "          PIT503    FIT601  P601  P602  P603  is_anomaly  \n",
       "0         3.3485  0.000256     1     1     1           0  \n",
       "1         3.3485  0.000256     1     1     1           0  \n",
       "2         3.3485  0.000256     1     1     1           0  \n",
       "3         3.3485  0.000256     1     1     1           0  \n",
       "4         3.3485  0.000256     1     1     1           0  \n",
       "...          ...       ...   ...   ...   ...         ...  \n",
       "494995  189.8552  0.000128     1     1     1           0  \n",
       "494996  189.5027  0.000128     1     1     1           0  \n",
       "494997  189.5027  0.000128     1     1     1           0  \n",
       "494998  189.5027  0.000128     1     1     1           0  \n",
       "494999  189.5988  0.000128     1     1     1           0  \n",
       "\n",
       "[495000 rows x 53 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\n",
    "    source_folder / \"SWaT.A1&A2_Dec 2015\" / \"Physical\" / \"SWaT_Dataset_Normal_v1.xlsx\",\n",
    "    sheet_name=0,\n",
    "    skiprows=1,\n",
    "    header=0,\n",
    "    index_col=None,\n",
    "    parse_dates=[0]\n",
    ")\n",
    "df[\"is_anomaly\"] = 0\n",
    "df.loc[df[\"Normal/Attack\"] == \"Attack\", \"is_anomaly\"] = 1\n",
    "df = df.drop(columns=[\"Normal/Attack\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tmp = df[[\"FIT101\", \"LIT101\", \"AIT203\", \"FIT201\", \"LIT401\", \"PIT503\", \"is_anomaly\"]]\n",
    "df_tmp.iloc[:, :-1].plot()\n",
    "s = df_tmp[\"is_anomaly\"].diff()\n",
    "for begin, end in zip(s[s == -1].index, s[s == 1].index):\n",
    "    plt.gca().add_patch(matplotlib.patches.Rectangle((begin, 0), end - begin, df_tmp.max().max(), color=\"red\", alpha=0.25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>FIT 101</th>\n",
       "      <th>LIT 101</th>\n",
       "      <th>MV 101</th>\n",
       "      <th>P1_STATE</th>\n",
       "      <th>P101 Status</th>\n",
       "      <th>P102 Status</th>\n",
       "      <th>AIT 201</th>\n",
       "      <th>AIT 202</th>\n",
       "      <th>AIT 203</th>\n",
       "      <th>...</th>\n",
       "      <th>LSH 601</th>\n",
       "      <th>LSH 602</th>\n",
       "      <th>LSH 603</th>\n",
       "      <th>LSL 601</th>\n",
       "      <th>LSL 602</th>\n",
       "      <th>LSL 603</th>\n",
       "      <th>P6 STATE</th>\n",
       "      <th>P601 Status</th>\n",
       "      <th>P602 Status</th>\n",
       "      <th>P603 Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-07-20 04:30:00+00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>729.865800</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>142.527557</td>\n",
       "      <td>9.293002</td>\n",
       "      <td>198.077423</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-07-20 04:30:01+00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>729.434000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>142.527557</td>\n",
       "      <td>9.293002</td>\n",
       "      <td>198.385025</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-07-20 04:30:02+00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>729.120000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>142.527557</td>\n",
       "      <td>9.293002</td>\n",
       "      <td>198.436300</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-07-20 04:30:03+00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>728.688200</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>142.527557</td>\n",
       "      <td>9.289157</td>\n",
       "      <td>198.667000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-07-20 04:30:04+00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>727.706900</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>142.527557</td>\n",
       "      <td>9.289157</td>\n",
       "      <td>198.897720</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>2019-07-20 08:39:55+00:00</td>\n",
       "      <td>4.200429</td>\n",
       "      <td>491.169769</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>131.408615</td>\n",
       "      <td>9.319918</td>\n",
       "      <td>257.703156</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>2019-07-20 08:39:56+00:00</td>\n",
       "      <td>4.253915</td>\n",
       "      <td>491.405273</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>131.408615</td>\n",
       "      <td>9.317354</td>\n",
       "      <td>257.703156</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>2019-07-20 08:39:57+00:00</td>\n",
       "      <td>4.303558</td>\n",
       "      <td>492.308100</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>131.408615</td>\n",
       "      <td>9.317354</td>\n",
       "      <td>257.703156</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>2019-07-20 08:39:58+00:00</td>\n",
       "      <td>4.323736</td>\n",
       "      <td>492.465100</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>131.408615</td>\n",
       "      <td>9.316713</td>\n",
       "      <td>257.703156</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>2019-07-20 08:39:59+00:00</td>\n",
       "      <td>4.323736</td>\n",
       "      <td>492.896881</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>131.408615</td>\n",
       "      <td>9.313829</td>\n",
       "      <td>257.933868</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14996 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      timestamp   FIT 101     LIT 101  MV 101  P1_STATE  \\\n",
       "0     2019-07-20 04:30:00+00:00  0.000000  729.865800       1         3   \n",
       "1     2019-07-20 04:30:01+00:00  0.000000  729.434000       1         3   \n",
       "2     2019-07-20 04:30:02+00:00  0.000000  729.120000       1         3   \n",
       "3     2019-07-20 04:30:03+00:00  0.000000  728.688200       1         3   \n",
       "4     2019-07-20 04:30:04+00:00  0.000000  727.706900       1         3   \n",
       "...                         ...       ...         ...     ...       ...   \n",
       "14991 2019-07-20 08:39:55+00:00  4.200429  491.169769       2         2   \n",
       "14992 2019-07-20 08:39:56+00:00  4.253915  491.405273       2         2   \n",
       "14993 2019-07-20 08:39:57+00:00  4.303558  492.308100       2         2   \n",
       "14994 2019-07-20 08:39:58+00:00  4.323736  492.465100       2         2   \n",
       "14995 2019-07-20 08:39:59+00:00  4.323736  492.896881       2         2   \n",
       "\n",
       "       P101 Status  P102 Status     AIT 201   AIT 202     AIT 203  ...  \\\n",
       "0                2            1  142.527557  9.293002  198.077423  ...   \n",
       "1                2            1  142.527557  9.293002  198.385025  ...   \n",
       "2                2            1  142.527557  9.293002  198.436300  ...   \n",
       "3                2            1  142.527557  9.289157  198.667000  ...   \n",
       "4                2            1  142.527557  9.289157  198.897720  ...   \n",
       "...            ...          ...         ...       ...         ...  ...   \n",
       "14991            2            1  131.408615  9.319918  257.703156  ...   \n",
       "14992            2            1  131.408615  9.317354  257.703156  ...   \n",
       "14993            2            1  131.408615  9.317354  257.703156  ...   \n",
       "14994            2            1  131.408615  9.316713  257.703156  ...   \n",
       "14995            2            1  131.408615  9.313829  257.933868  ...   \n",
       "\n",
       "       LSH 601  LSH 602  LSH 603  LSL 601  LSL 602  LSL 603  P6 STATE  \\\n",
       "0            1        1        0        0        0        1         2   \n",
       "1            1        1        0        0        0        1         2   \n",
       "2            1        1        0        0        0        1         2   \n",
       "3            1        1        0        0        0        1         2   \n",
       "4            1        1        0        0        0        1         2   \n",
       "...        ...      ...      ...      ...      ...      ...       ...   \n",
       "14991        1        1        0        0        0        1         2   \n",
       "14992        1        1        0        0        0        1         2   \n",
       "14993        1        1        0        0        0        1         2   \n",
       "14994        1        1        0        0        0        1         2   \n",
       "14995        1        1        0        0        0        1         2   \n",
       "\n",
       "       P601 Status  P602 Status  P603 Status  \n",
       "0                1            1            1  \n",
       "1                1            1            1  \n",
       "2                1            1            1  \n",
       "3                1            1            1  \n",
       "4                1            1            1  \n",
       "...            ...          ...          ...  \n",
       "14991            1            1            1  \n",
       "14992            1            1            1  \n",
       "14993            1            1            1  \n",
       "14994            1            1            1  \n",
       "14995            1            1            1  \n",
       "\n",
       "[14996 rows x 78 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\n",
    "    source_folder / \"SWaT.A4&A5_Jul 2019\" / \"SWaT_dataset_Jul 19 v2.xlsx\",\n",
    "    sheet_name=0,\n",
    "    skiprows=1,\n",
    "    header=[0, 1],\n",
    "    index_col=None,\n",
    "    parse_dates=[0]\n",
    ")\n",
    "\n",
    "# align header\n",
    "df.columns = list(df.columns.get_level_values(1)[0:1]) + list(df.columns.get_level_values(0)[1:])\n",
    "\n",
    "# round timestamps to have equi-distant timesteps (= seconds)\n",
    "df[\"timestamp\"] = df[\"timestamp\"].map(lambda dt: dt.floor(freq=\"S\"))\n",
    "\n",
    "df = df.replace(\"Inactive\", 0)\n",
    "df = df.replace(\"Active\", 1)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define anomalies\n",
    "anomalies = {\n",
    "    \"Attack1\": {\n",
    "        \"description\": \"Spoof value of FIT401 from 0.8 to 0.5 to stop de-chlorination by switching off UV401\",\n",
    "        \"targets\": [\"FIT 401\"],\n",
    "        \"begin\": \"2019-07-20 15:08:46+08:00\",\n",
    "        \"end\": \"2019-07-20 15:10:31+08:00\"\n",
    "    },\n",
    "    \"Attack2\": {\n",
    "        \"description\": \" Spoof value of LIT301 from 835 to 1024 to eventually lead to underflow in T301\",\n",
    "        \"targets\": [\"LIT 301\"],\n",
    "        \"begin\": \"2019-07-20 15:15:00+08:00\",\n",
    "        \"end\": \"2019-07-20 15:19:32+08:00\"\n",
    "    },\n",
    "    \"Attack3\": {\n",
    "        \"description\": \"Switch P601 from OFF to ON to increase water in raw water tank\",\n",
    "        \"targets\": [\"P601 Status\"],\n",
    "        \"begin\": \"2019-07-20 15:26:57+08:00\",\n",
    "        \"end\": \"2019-07-20 15:30:48+08:00\"\n",
    "    },\n",
    "    \"Attack4\": {\n",
    "        \"description\": \"Switch from CLOSE to OPEN (MV201) and OFF to ON (P101) to overflow tank T301\",\n",
    "        \"targets\": [\"MV201\", \"P101 Status\"],\n",
    "        \"begin\": \"2019-07-20 15:38:50+08:00\",\n",
    "        \"end\": \"2019-07-20 15:46:20+08:00\"\n",
    "    },\n",
    "    \"Attack5\": {\n",
    "        \"description\": \"Switch MV501 from OPEN to CLOSE to drain water from RO\",\n",
    "        \"targets\": [\"MV 501\"],\n",
    "        \"begin\": \"2019-07-20 15:54:00+08:00\",\n",
    "        \"end\": \"2019-07-20 15:56:00+08:00\"\n",
    "    },\n",
    "    \"Attack6\": {\n",
    "        \"description\": \"Switch P301 from ON to OFF to halt stage 3 (UF process)\",\n",
    "        \"targets\": [\"P301 Status\"],\n",
    "        \"begin\": \"2019-07-20 16:02:56+08:00\",\n",
    "        \"end\": \"2019-07-20 16:16:18+08:00\"\n",
    "    },\n",
    "}\n",
    "\n",
    "for a in anomalies:\n",
    "    for dt_key in [\"begin\", \"end\"]:\n",
    "        # convert times to UTZ-datetimes\n",
    "        dt = pd.to_datetime(anomalies[a][dt_key]).astimezone(tz=\"UTC\")\n",
    "        \n",
    "        # fix anomaly labels (timestamps)\n",
    "        dt = dt - pd.Timedelta(2, unit=\"m\") + pd.Timedelta(14, unit=\"s\")\n",
    "        anomalies[a][dt_key] = dt\n",
    "    if a == \"Attack1\":\n",
    "        anomalies[a][\"begin\"] -= pd.Timedelta(2, unit=\"s\")\n",
    "        anomalies[a][\"end\"] -= pd.Timedelta(1, unit=\"s\")\n",
    "    elif a == \"Attack2\":\n",
    "        anomalies[a][\"begin\"] += pd.Timedelta(5, unit=\"s\")\n",
    "    elif a == \"Attack4\":\n",
    "        anomalies[a][\"end\"] += pd.Timedelta(30, unit=\"s\")\n",
    "    elif a == \"Attack5\":\n",
    "        anomalies[a][\"begin\"] += pd.Timedelta(3, unit=\"s\")\n",
    "        anomalies[a][\"end\"] += pd.Timedelta(40, unit=\"s\")\n",
    "    elif a == \"Attack6\":\n",
    "        anomalies[a][\"begin\"] -= pd.Timedelta(5, unit=\"s\")\n",
    "        anomalies[a][\"end\"] += pd.Timedelta(35, unit=\"s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>FIT 101</th>\n",
       "      <th>LIT 101</th>\n",
       "      <th>MV 101</th>\n",
       "      <th>P1_STATE</th>\n",
       "      <th>P101 Status</th>\n",
       "      <th>P102 Status</th>\n",
       "      <th>AIT 201</th>\n",
       "      <th>AIT 202</th>\n",
       "      <th>AIT 203</th>\n",
       "      <th>...</th>\n",
       "      <th>LSH 602</th>\n",
       "      <th>LSH 603</th>\n",
       "      <th>LSL 601</th>\n",
       "      <th>LSL 602</th>\n",
       "      <th>LSL 603</th>\n",
       "      <th>P6 STATE</th>\n",
       "      <th>P601 Status</th>\n",
       "      <th>P602 Status</th>\n",
       "      <th>P603 Status</th>\n",
       "      <th>is_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-07-20 04:30:00+00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>729.865800</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>142.527557</td>\n",
       "      <td>9.293002</td>\n",
       "      <td>198.077423</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-07-20 04:30:01+00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>729.434000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>142.527557</td>\n",
       "      <td>9.293002</td>\n",
       "      <td>198.385025</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-07-20 04:30:02+00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>729.120000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>142.527557</td>\n",
       "      <td>9.293002</td>\n",
       "      <td>198.436300</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-07-20 04:30:03+00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>728.688200</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>142.527557</td>\n",
       "      <td>9.289157</td>\n",
       "      <td>198.667000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-07-20 04:30:04+00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>727.706900</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>142.527557</td>\n",
       "      <td>9.289157</td>\n",
       "      <td>198.897720</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>2019-07-20 08:39:55+00:00</td>\n",
       "      <td>4.200429</td>\n",
       "      <td>491.169769</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>131.408615</td>\n",
       "      <td>9.319918</td>\n",
       "      <td>257.703156</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>2019-07-20 08:39:56+00:00</td>\n",
       "      <td>4.253915</td>\n",
       "      <td>491.405273</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>131.408615</td>\n",
       "      <td>9.317354</td>\n",
       "      <td>257.703156</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>2019-07-20 08:39:57+00:00</td>\n",
       "      <td>4.303558</td>\n",
       "      <td>492.308100</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>131.408615</td>\n",
       "      <td>9.317354</td>\n",
       "      <td>257.703156</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>2019-07-20 08:39:58+00:00</td>\n",
       "      <td>4.323736</td>\n",
       "      <td>492.465100</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>131.408615</td>\n",
       "      <td>9.316713</td>\n",
       "      <td>257.703156</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>2019-07-20 08:39:59+00:00</td>\n",
       "      <td>4.323736</td>\n",
       "      <td>492.896881</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>131.408615</td>\n",
       "      <td>9.313829</td>\n",
       "      <td>257.933868</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14996 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      timestamp   FIT 101     LIT 101  MV 101  P1_STATE  \\\n",
       "0     2019-07-20 04:30:00+00:00  0.000000  729.865800       1         3   \n",
       "1     2019-07-20 04:30:01+00:00  0.000000  729.434000       1         3   \n",
       "2     2019-07-20 04:30:02+00:00  0.000000  729.120000       1         3   \n",
       "3     2019-07-20 04:30:03+00:00  0.000000  728.688200       1         3   \n",
       "4     2019-07-20 04:30:04+00:00  0.000000  727.706900       1         3   \n",
       "...                         ...       ...         ...     ...       ...   \n",
       "14991 2019-07-20 08:39:55+00:00  4.200429  491.169769       2         2   \n",
       "14992 2019-07-20 08:39:56+00:00  4.253915  491.405273       2         2   \n",
       "14993 2019-07-20 08:39:57+00:00  4.303558  492.308100       2         2   \n",
       "14994 2019-07-20 08:39:58+00:00  4.323736  492.465100       2         2   \n",
       "14995 2019-07-20 08:39:59+00:00  4.323736  492.896881       2         2   \n",
       "\n",
       "       P101 Status  P102 Status     AIT 201   AIT 202     AIT 203  ...  \\\n",
       "0                2            1  142.527557  9.293002  198.077423  ...   \n",
       "1                2            1  142.527557  9.293002  198.385025  ...   \n",
       "2                2            1  142.527557  9.293002  198.436300  ...   \n",
       "3                2            1  142.527557  9.289157  198.667000  ...   \n",
       "4                2            1  142.527557  9.289157  198.897720  ...   \n",
       "...            ...          ...         ...       ...         ...  ...   \n",
       "14991            2            1  131.408615  9.319918  257.703156  ...   \n",
       "14992            2            1  131.408615  9.317354  257.703156  ...   \n",
       "14993            2            1  131.408615  9.317354  257.703156  ...   \n",
       "14994            2            1  131.408615  9.316713  257.703156  ...   \n",
       "14995            2            1  131.408615  9.313829  257.933868  ...   \n",
       "\n",
       "       LSH 602  LSH 603  LSL 601  LSL 602  LSL 603  P6 STATE  P601 Status  \\\n",
       "0            1        0        0        0        1         2            1   \n",
       "1            1        0        0        0        1         2            1   \n",
       "2            1        0        0        0        1         2            1   \n",
       "3            1        0        0        0        1         2            1   \n",
       "4            1        0        0        0        1         2            1   \n",
       "...        ...      ...      ...      ...      ...       ...          ...   \n",
       "14991        1        0        0        0        1         2            1   \n",
       "14992        1        0        0        0        1         2            1   \n",
       "14993        1        0        0        0        1         2            1   \n",
       "14994        1        0        0        0        1         2            1   \n",
       "14995        1        0        0        0        1         2            1   \n",
       "\n",
       "       P602 Status  P603 Status  is_anomaly  \n",
       "0                1            1           0  \n",
       "1                1            1           0  \n",
       "2                1            1           0  \n",
       "3                1            1           0  \n",
       "4                1            1           0  \n",
       "...            ...          ...         ...  \n",
       "14991            1            1           0  \n",
       "14992            1            1           0  \n",
       "14993            1            1           0  \n",
       "14994            1            1           0  \n",
       "14995            1            1           0  \n",
       "\n",
       "[14996 rows x 79 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index(\"timestamp\")\n",
    "\n",
    "df[\"is_anomaly\"] = 0\n",
    "\n",
    "for a in anomalies:\n",
    "    begin = anomalies[a][\"begin\"]\n",
    "    end = anomalies[a][\"end\"]\n",
    "    df.loc[begin:end, \"is_anomaly\"] = 1\n",
    "df = df.reset_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "enlarge_x = 120\n",
    "for i in range(6):\n",
    "    a = list(anomalies.keys())[i]\n",
    "    a = anomalies[a]\n",
    "    targets = a[\"targets\"]\n",
    "    begin = a[\"begin\"] - pd.Timedelta(enlarge_x, unit=\"s\")\n",
    "    end = a[\"end\"] + pd.Timedelta(enlarge_x, unit=\"s\")\n",
    "\n",
    "    df_tmp = df.set_index(\"timestamp\")\n",
    "    df_tmp = df_tmp.loc[begin:end, targets + [\"is_anomaly\"]]\n",
    "    df_tmp.iloc[:, :-1].plot()\n",
    "    s = df_tmp[\"is_anomaly\"].diff()\n",
    "    for begin, end in zip(s[s == -1].index, s[s == 1].index):\n",
    "        plt.gca().add_patch(matplotlib.patches.Rectangle((begin, 0), end - begin, df_tmp.max().max(), color=\"red\", alpha=0.25))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>P1_STATE</th>\n",
       "      <th>LIT101.Pv</th>\n",
       "      <th>FIT101.Pv</th>\n",
       "      <th>MV101.Status</th>\n",
       "      <th>P101.Status</th>\n",
       "      <th>P102.Status</th>\n",
       "      <th>P2_STATE</th>\n",
       "      <th>FIT201.Pv</th>\n",
       "      <th>AIT201.Pv</th>\n",
       "      <th>...</th>\n",
       "      <th>FIT601.Pv</th>\n",
       "      <th>P601.Status</th>\n",
       "      <th>P602.Status</th>\n",
       "      <th>P603.Status</th>\n",
       "      <th>LSH601.Alarm</th>\n",
       "      <th>LSL601.Alarm</th>\n",
       "      <th>LSH602.Alarm</th>\n",
       "      <th>LSL602.Alarm</th>\n",
       "      <th>LSH603.Alarm</th>\n",
       "      <th>LSL603.Alarm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-06 10:05:00+08:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>658.661255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.313523</td>\n",
       "      <td>35.215330</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-12-06 10:05:01+08:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>659.171600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.311857</td>\n",
       "      <td>35.215330</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-06 10:05:02+08:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>659.681800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.311601</td>\n",
       "      <td>35.215330</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-06 10:05:03+08:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>660.349100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.310448</td>\n",
       "      <td>35.215330</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-06 10:05:04+08:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>660.780945</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.310448</td>\n",
       "      <td>35.215330</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13196</th>\n",
       "      <td>2019-12-06 13:44:56+08:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>704.704800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.332233</td>\n",
       "      <td>27.396822</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13197</th>\n",
       "      <td>2019-12-06 13:44:57+08:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>703.801941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.333643</td>\n",
       "      <td>27.396822</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13198</th>\n",
       "      <td>2019-12-06 13:44:58+08:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>703.016900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.333643</td>\n",
       "      <td>27.396822</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13199</th>\n",
       "      <td>2019-12-06 13:44:59+08:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>701.996338</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.333643</td>\n",
       "      <td>27.396822</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13200</th>\n",
       "      <td>2019-12-06 13:45:00+08:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>701.368300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.330055</td>\n",
       "      <td>27.396822</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13201 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      timestamp  P1_STATE   LIT101.Pv  FIT101.Pv  \\\n",
       "0     2019-12-06 10:05:00+08:00       3.0  658.661255        0.0   \n",
       "1     2019-12-06 10:05:01+08:00       3.0  659.171600        0.0   \n",
       "2     2019-12-06 10:05:02+08:00       3.0  659.681800        0.0   \n",
       "3     2019-12-06 10:05:03+08:00       3.0  660.349100        0.0   \n",
       "4     2019-12-06 10:05:04+08:00       3.0  660.780945        0.0   \n",
       "...                         ...       ...         ...        ...   \n",
       "13196 2019-12-06 13:44:56+08:00       2.0  704.704800        0.0   \n",
       "13197 2019-12-06 13:44:57+08:00       2.0  703.801941        0.0   \n",
       "13198 2019-12-06 13:44:58+08:00       2.0  703.016900        0.0   \n",
       "13199 2019-12-06 13:44:59+08:00       2.0  701.996338        0.0   \n",
       "13200 2019-12-06 13:45:00+08:00       2.0  701.368300        0.0   \n",
       "\n",
       "       MV101.Status  P101.Status  P102.Status  P2_STATE  FIT201.Pv  AIT201.Pv  \\\n",
       "0               1.0          2.0          1.0       2.0   2.313523  35.215330   \n",
       "1               1.0          2.0          1.0       2.0   2.311857  35.215330   \n",
       "2               1.0          2.0          1.0       2.0   2.311601  35.215330   \n",
       "3               1.0          2.0          1.0       2.0   2.310448  35.215330   \n",
       "4               1.0          2.0          1.0       2.0   2.310448  35.215330   \n",
       "...             ...          ...          ...       ...        ...        ...   \n",
       "13196           1.0          2.0          1.0       2.0   2.332233  27.396822   \n",
       "13197           1.0          2.0          1.0       2.0   2.333643  27.396822   \n",
       "13198           1.0          2.0          1.0       2.0   2.333643  27.396822   \n",
       "13199           1.0          2.0          1.0       2.0   2.333643  27.396822   \n",
       "13200           1.0          2.0          1.0       2.0   2.330055  27.396822   \n",
       "\n",
       "       ...  FIT601.Pv  P601.Status  P602.Status  P603.Status  LSH601.Alarm  \\\n",
       "0      ...   0.000256          2.0          1.0          1.0             0   \n",
       "1      ...   0.000256          2.0          1.0          1.0             0   \n",
       "2      ...   0.000256          2.0          1.0          1.0             0   \n",
       "3      ...   0.000256          2.0          1.0          1.0             0   \n",
       "4      ...   0.000256          2.0          1.0          1.0             0   \n",
       "...    ...        ...          ...          ...          ...           ...   \n",
       "13196  ...   0.000256          1.0          1.0          1.0             0   \n",
       "13197  ...   0.000256          1.0          1.0          1.0             0   \n",
       "13198  ...   0.000256          1.0          1.0          1.0             0   \n",
       "13199  ...   0.000256          1.0          1.0          1.0             0   \n",
       "13200  ...   0.000256          1.0          1.0          1.0             0   \n",
       "\n",
       "       LSL601.Alarm  LSH602.Alarm  LSL602.Alarm  LSH603.Alarm  LSL603.Alarm  \n",
       "0                 0             1             0             0             1  \n",
       "1                 0             1             0             0             1  \n",
       "2                 0             1             0             0             1  \n",
       "3                 0             1             0             0             1  \n",
       "4                 0             1             0             0             1  \n",
       "...             ...           ...           ...           ...           ...  \n",
       "13196             0             1             0             0             1  \n",
       "13197             0             1             0             0             1  \n",
       "13198             0             1             0             0             1  \n",
       "13199             0             1             0             0             1  \n",
       "13200             0             1             0             0             1  \n",
       "\n",
       "[13201 rows x 82 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\n",
    "    source_folder / \"SWaT.A6_Dec 2019\" / \"csv\" / \"Dec2019.xlsx\",\n",
    "    sheet_name=0,\n",
    "    skiprows=9,\n",
    "    header=0,\n",
    "    index_col=None,\n",
    "    parse_dates=[0]\n",
    ")\n",
    "\n",
    "# localize timestamp\n",
    "s = df[\"t_stamp\"]\n",
    "s = s.map(lambda dt: dt.tz_localize(tz=\"Asia/Hong_Kong\"))\n",
    "df.insert(0, \"timestamp\", s)\n",
    "df.drop(columns=[\"t_stamp\"], inplace=True)\n",
    "\n",
    "df = df.replace(\"Inactive\", 0)\n",
    "df = df.replace(\"Active\", 1)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "anomalies = {\n",
    "# ignore those attacks, because we could not find any evidence in the sensor data\n",
    "#    \"Infection\": {\n",
    "#        \"description\": \"Infiltrate SCADA WS via USB\",\n",
    "#        \"begin\": \"10:20\",\n",
    "#        \"end\": \"10:30\"\n",
    "#    },\n",
    "#    \"Attack1\": {\n",
    "#        \"description\": \"Exfiltrate data cycle 1\",\n",
    "#        \"begin\": \"10:30\",\n",
    "#        \"end\": \"10:35\"\n",
    "#    },\n",
    "#    \"Attack2\": {\n",
    "#        \"description\": \"Exfiltrate data cycle 2\",\n",
    "#        \"begin\": \"10:45\",\n",
    "#        \"end\": \"10:50\"\n",
    "#    },\n",
    "#    \"Attack3\": {\n",
    "#        \"description\": \"Exfiltrate data cycle 3\",\n",
    "#        \"begin\": \"11:00\",\n",
    "#        \"end\": \"11:05\"\n",
    "#    },\n",
    "#    \"Attack4\": {\n",
    "#        \"description\": \"Exfiltrate data cycle 4\",\n",
    "#        \"begin\": \"11:15\",\n",
    "#        \"end\": \"11:20\"\n",
    "#    },\n",
    "    \"C2Com&Attack5\": {\n",
    "        \"description\": \"Infiltrate SCADA WS with second payload from C2, and disrupt sensors cycle 1\",\n",
    "        \"begin\": \"12:30\",\n",
    "        \"end\": \"12:33\"\n",
    "    },\n",
    "    \"Attack6\": {\n",
    "        \"description\": \"Disrupt sensors cycle 2\",\n",
    "        \"begin\": \"12:43\",\n",
    "        \"end\": \"12:46\"\n",
    "    },\n",
    "    \"Attack7\": {\n",
    "        \"description\": \"Disrupt sensors cycle 3\",\n",
    "        \"begin\": \"12:56\",\n",
    "        \"end\": \"12:59\"\n",
    "    },\n",
    "    \"Attack8\": {\n",
    "        \"description\": \"Disrupt sensors cycle 4\",\n",
    "        \"begin\": \"13:09\",\n",
    "        \"end\": \"13:12\"\n",
    "    },\n",
    "    \"Attack9\": {\n",
    "        \"description\": \"Disrupt sensors cycle 5\",\n",
    "        \"begin\": \"13:22\",\n",
    "        \"end\": \"13:25\"\n",
    "    }\n",
    "}\n",
    "\n",
    "date = \"2019-12-06\"\n",
    "tz = \"Asia/Hong_Kong\"\n",
    "\n",
    "for a in anomalies:\n",
    "    for dt_key in [\"begin\", \"end\"]:\n",
    "        # convert times to datetimes\n",
    "        dt = pd.to_datetime(f\"{date} {anomalies[a][dt_key]}:00\").tz_localize(tz=tz)\n",
    "        anomalies[a][dt_key] = dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>P1_STATE</th>\n",
       "      <th>LIT101.Pv</th>\n",
       "      <th>FIT101.Pv</th>\n",
       "      <th>MV101.Status</th>\n",
       "      <th>P101.Status</th>\n",
       "      <th>P102.Status</th>\n",
       "      <th>P2_STATE</th>\n",
       "      <th>FIT201.Pv</th>\n",
       "      <th>AIT201.Pv</th>\n",
       "      <th>...</th>\n",
       "      <th>P601.Status</th>\n",
       "      <th>P602.Status</th>\n",
       "      <th>P603.Status</th>\n",
       "      <th>LSH601.Alarm</th>\n",
       "      <th>LSL601.Alarm</th>\n",
       "      <th>LSH602.Alarm</th>\n",
       "      <th>LSL602.Alarm</th>\n",
       "      <th>LSH603.Alarm</th>\n",
       "      <th>LSL603.Alarm</th>\n",
       "      <th>is_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-06 10:05:00+08:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>658.661255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.313523</td>\n",
       "      <td>35.215330</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-12-06 10:05:01+08:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>659.171600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.311857</td>\n",
       "      <td>35.215330</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-06 10:05:02+08:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>659.681800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.311601</td>\n",
       "      <td>35.215330</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-06 10:05:03+08:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>660.349100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.310448</td>\n",
       "      <td>35.215330</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-06 10:05:04+08:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>660.780945</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.310448</td>\n",
       "      <td>35.215330</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13196</th>\n",
       "      <td>2019-12-06 13:44:56+08:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>704.704800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.332233</td>\n",
       "      <td>27.396822</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13197</th>\n",
       "      <td>2019-12-06 13:44:57+08:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>703.801941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.333643</td>\n",
       "      <td>27.396822</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13198</th>\n",
       "      <td>2019-12-06 13:44:58+08:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>703.016900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.333643</td>\n",
       "      <td>27.396822</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13199</th>\n",
       "      <td>2019-12-06 13:44:59+08:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>701.996338</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.333643</td>\n",
       "      <td>27.396822</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13200</th>\n",
       "      <td>2019-12-06 13:45:00+08:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>701.368300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.330055</td>\n",
       "      <td>27.396822</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13201 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      timestamp  P1_STATE   LIT101.Pv  FIT101.Pv  \\\n",
       "0     2019-12-06 10:05:00+08:00       3.0  658.661255        0.0   \n",
       "1     2019-12-06 10:05:01+08:00       3.0  659.171600        0.0   \n",
       "2     2019-12-06 10:05:02+08:00       3.0  659.681800        0.0   \n",
       "3     2019-12-06 10:05:03+08:00       3.0  660.349100        0.0   \n",
       "4     2019-12-06 10:05:04+08:00       3.0  660.780945        0.0   \n",
       "...                         ...       ...         ...        ...   \n",
       "13196 2019-12-06 13:44:56+08:00       2.0  704.704800        0.0   \n",
       "13197 2019-12-06 13:44:57+08:00       2.0  703.801941        0.0   \n",
       "13198 2019-12-06 13:44:58+08:00       2.0  703.016900        0.0   \n",
       "13199 2019-12-06 13:44:59+08:00       2.0  701.996338        0.0   \n",
       "13200 2019-12-06 13:45:00+08:00       2.0  701.368300        0.0   \n",
       "\n",
       "       MV101.Status  P101.Status  P102.Status  P2_STATE  FIT201.Pv  AIT201.Pv  \\\n",
       "0               1.0          2.0          1.0       2.0   2.313523  35.215330   \n",
       "1               1.0          2.0          1.0       2.0   2.311857  35.215330   \n",
       "2               1.0          2.0          1.0       2.0   2.311601  35.215330   \n",
       "3               1.0          2.0          1.0       2.0   2.310448  35.215330   \n",
       "4               1.0          2.0          1.0       2.0   2.310448  35.215330   \n",
       "...             ...          ...          ...       ...        ...        ...   \n",
       "13196           1.0          2.0          1.0       2.0   2.332233  27.396822   \n",
       "13197           1.0          2.0          1.0       2.0   2.333643  27.396822   \n",
       "13198           1.0          2.0          1.0       2.0   2.333643  27.396822   \n",
       "13199           1.0          2.0          1.0       2.0   2.333643  27.396822   \n",
       "13200           1.0          2.0          1.0       2.0   2.330055  27.396822   \n",
       "\n",
       "       ...  P601.Status  P602.Status  P603.Status  LSH601.Alarm  LSL601.Alarm  \\\n",
       "0      ...          2.0          1.0          1.0             0             0   \n",
       "1      ...          2.0          1.0          1.0             0             0   \n",
       "2      ...          2.0          1.0          1.0             0             0   \n",
       "3      ...          2.0          1.0          1.0             0             0   \n",
       "4      ...          2.0          1.0          1.0             0             0   \n",
       "...    ...          ...          ...          ...           ...           ...   \n",
       "13196  ...          1.0          1.0          1.0             0             0   \n",
       "13197  ...          1.0          1.0          1.0             0             0   \n",
       "13198  ...          1.0          1.0          1.0             0             0   \n",
       "13199  ...          1.0          1.0          1.0             0             0   \n",
       "13200  ...          1.0          1.0          1.0             0             0   \n",
       "\n",
       "       LSH602.Alarm  LSL602.Alarm  LSH603.Alarm  LSL603.Alarm  is_anomaly  \n",
       "0                 1             0             0             1           0  \n",
       "1                 1             0             0             1           0  \n",
       "2                 1             0             0             1           0  \n",
       "3                 1             0             0             1           0  \n",
       "4                 1             0             0             1           0  \n",
       "...             ...           ...           ...           ...         ...  \n",
       "13196             1             0             0             1           0  \n",
       "13197             1             0             0             1           0  \n",
       "13198             1             0             0             1           0  \n",
       "13199             1             0             0             1           0  \n",
       "13200             1             0             0             1           0  \n",
       "\n",
       "[13201 rows x 83 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index(\"timestamp\")\n",
    "\n",
    "df[\"is_anomaly\"] = 0\n",
    "\n",
    "for a in anomalies:\n",
    "    begin = anomalies[a][\"begin\"]\n",
    "    end = anomalies[a][\"end\"]\n",
    "    df.loc[begin:end, \"is_anomaly\"] = 1\n",
    "df = df.reset_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "columns = [c for c in df.columns if c.endswith(\"Pv\")][:5]\n",
    "df_tmp = df.loc[7000:, columns].copy()\n",
    "plt.Figure()\n",
    "df_tmp.plot()\n",
    "s = df[\"is_anomaly\"].diff()\n",
    "for begin, end in zip(s[s == -1].index, s[s == 1].index):\n",
    "    plt.gca().add_patch(matplotlib.patches.Rectangle((begin, 0), end - begin, df_tmp.max().max(), color=\"red\", alpha=0.25))\n",
    "plt.gca().set_ylim(0, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "timeeval",
   "language": "python",
   "name": "timeeval"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
